Networks are ubiquitous and are popular mathematical abstractions to analyze data, particularly web data. The natural interconnectedness that web data, like social and information networks, presents make networks a natural metaphor to understand the relationships exhibited in the high-volume multi-modal nature of data. Thus most analytics research for networks such as link prediction [1]} or node classification [2]} involving machine learning, require features that are descriptive and discriminative. Thus it is important to translate complex graph data into a set of attributes through creative feature engineering, which can often be difficult to compute, especially for high volume of data.
